nw-ab-critique-dimensi…
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions and bug patterns for journey artifact reviews
$ npx -y skills add nWave-ai/nWave --skill nw-por-review-criteria --agent claude-codeHow it fires
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/nw-por-review-criteriaContext preview
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Review dimensions and bug patterns for journey artifact reviews
name: nw-por-review-criteria description: Review dimensions and bug patterns for journey artifact reviews user-invocable: false disable-model-invocation: true
Domain knowledge for product-owner-reviewer (Eclipse). Covers journey coherence, emotional arcs, shared artifacts, example data quality, CLI UX patterns.
Validate complete flow with no gaps.
Checks: all steps start-to-goal defined | no orphan steps | no dead ends | decision branches lead somewhere | error paths guide to recovery
Severity: critical = missing main flow steps / dead ends | high = orphan steps | medium = ambiguous decisions | low = minor clarity
Validate emotional design quality.
Checks: arc defined (start/middle/end) | all steps annotated | no jarring transitions | confidence builds progressively | error states guide not frustrate
Severity: critical = no arc / major jarring transitions | high = missing key annotations | medium = confidence doesn't build | low = minor polish
Validate ${variable} sources and consistency.
Checks: all ${variables} have documented source | single source of truth | all consumers listed | integration risks assessed | validation methods specified
Severity: critical = undocumented ${variables} / multiple sources | high = missing consumers / unassessed risks | medium = incomplete validation | low = minor consumer docs
Key review skill -- analyze data for integration gaps.
Checks: realistic not generic | reveals integration dependencies | catches version mismatches | catches path inconsistencies | consistent across steps
Severity: critical = generic placeholders hide issues | high = inconsistent across steps | medium = doesn't reveal deps | low = could be more realistic
Apply: 1) trace ${version} through all steps -- same? 2) compare ${install_path} step 2 vs 3 -- match? 3) does data show actual integration points?
Generic "v1.0.0" or "/path/to/install" hides bugs. Realistic "v1.2.86" from "pyproject.toml" reveals bugs.
Checks: command vocabulary consistent | help available | error messages guide to resolution | progressive disclosure respected
Severity: critical = inconsistent commands | high = no error recovery guidance | medium = missing progressive disclosure | low = minor vocabulary
Multiple version sources. Trace ${version} through all steps -- same source?
Step 1: v${version} from pyproject.toml
Step 2: v${version} from version.txt <-- MISMATCHURLs without canonical source. For each URL: "where is this defined?"
Install: git+https://github.com/org/repo <-- Where is this URL canonically defined?
Paths from different sources. Trace ${path} -- same source?
Install to: ${install_path} from config
Uninstall from: ~/.claude/agents/nw/ <-- HARDCODEDCLI commands without slash equivalents. Check both contexts exist.
Terminal: crafter run Claude Code: /nw-execute <-- EXISTS?
review_id: "{timestamp}"
reviewer: "nw-product-owner-reviewer (Eclipse)"
artifact_reviewed: "{file path}"
strengths:
- strength: "{Positive aspect}"
example: "{Specific evidence}"
issues_identified:
journey_coherence:
- issue: "{Description}"
severity: "critical|high|medium|low"
location: "{Where}"
recommendation: "{Fix}"
emotional_arc:
- issue: "{Description}"
severity: "critical|high|medium|low"
location: "{Where}"
recommendation: "{Fix}"
shared_artifacts:
- issue: "{Description}"
severity: "critical|high|medium|low"
artifact: "{Which ${variable}}"
recommendation: "{Fix}"
example_data:
- issue: "{Description}"
severity: "critical|high|medium|low"
data_point: "{Which data}"
integration_risk: "{What bug it might hide}"
recommendation: "{Fix}"
bug_patterns_detected:
- pattern: "version_mismatch|hardcoded_url|path_inconsistency|missing_command"
severity: "critical|high"
evidence: "{Finding}"
recommendation: "{Fix}"
recommendations:
critical: ["{Must fix before approval}"]
high: ["{Should fix before approval}"]
medium: ["{Fix in next iteration}"]
low: ["{Consider for polish}"]
approval_status: "approved|rejected_pending_revisions|conditionally_approved"
approval_conditions: "{If conditional, what must be done}"AI agents that guide you from idea to working code, with human judgment at every gate. nWave runs inside Claude Code. It breaks feature delivery into seven waves (discover, diverge, discuss, design, devops, distill, deliver).
Repo: nWave-ai/nWave
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
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